Conveyor alternative for AI questionnaire accuracy

• Accuracy in AI questionnaire tools is mostly an architecture question: where the answer comes from, whether it's traceable to an approved source, and how conflicts get resolved.
• A model swap doesn't fix a bad architecture. Two tools running the same underlying LLM can produce very different accuracy if one grounds answers in extracted facts and the other freely generates text from a document pile.
• The questions in this post apply to any vendor, including Conveyor. Use them in a bake-off instead of taking accuracy claims at face value.
• Wolfia's approach: fact extraction with source attribution on every answer, conflict resolution scored by source authority, data network effects that compound accuracy over time, and a human-in-the-loop review step before answers ship.
• No accuracy benchmark numbers are cited here for either product, because none are independently verifiable at the moment. Ask any vendor for a live demo on your own documents instead of trusting a marketing percentage.

Every AI questionnaire vendor, Conveyor included, will tell you their tool is accurate. That claim is nearly impossible to verify from the outside because "accuracy" depends entirely on what happens before the model ever generates a word. Two products can run comparable large language models and still differ enormously in reliability, because the differentiator sits in the retrieval and grounding layer, not the model layer.

That's the reframe GRC buyers need before evaluating any tool: stop asking "which model do you use" and start asking "where does this specific answer come from, and can I verify it." On the questionnaires we process at Wolfia, the recurring failure mode we see in other tools isn't a dumb model. It's an architecture that can't tell a reviewer which source document backs a given sentence, so the reviewer has to re-verify the answer from scratch anyway, which erases the time savings automation was supposed to deliver.

The direct answer: a trustworthy tool traces every generated answer back to a specific, approved source document, not to a blended summary the model wrote on the fly. If a vendor can't show you the exact paragraph an answer came from, you're looking at free-form generation dressed up as automation.

This is the single biggest architectural fork in the category. Plain retrieval-augmented generation (RAG) pulls a handful of relevant document chunks and asks the model to synthesize a response in its own words. That synthesis step is where hallucination risk lives, because the model is paraphrasing rather than citing. Fact extraction is a different approach: the system pulls a discrete, previously-approved fact (a specific control statement, a specific data retention window, a specific certification date) and attaches its source. A reviewer checks a citation instead of re-reading a paragraph to see if it's still true.

Wolfia's knowledge management layer is built around fact extraction, and every answer carries a citation to the approved source it came from, so reviewers verify rather than trust. That's a meaningfully different guarantee than "we used a good model."

The direct answer: the system should surface the contradiction and resolve it by source authority, not silently pick one side. If a security policy says one retention window and a signed customer contract says another, an accurate tool flags that conflict for a human instead of guessing.

Every knowledge base that spans more than a handful of documents accumulates contradictions eventually. A SOC 2 report gets updated. A DPA gets renegotiated with new terms. An internal wiki page goes stale while the policy document underneath it changes. A tool that treats all source documents as equally trustworthy will eventually generate an answer that's technically sourced but practically wrong, because it pulled from the outdated document instead of the current one.

Wolfia scores sources by authority and surfaces contradictions explicitly rather than silently resolving them, which is the difference between "grounded in a document" and "grounded in the right document." This is the question to ask any vendor in a bake-off: load two contradictory versions of the same policy and see what the tool does. If it doesn't flag the conflict, ask what it's doing instead.

The direct answer: it depends entirely on whether the system is built to learn from usage. A tool built around static document retrieval performs the same on day 400 as it did on day one, unless someone manually re-tags and re-uploads documents. A tool built around data network effects gets measurably better as more questionnaires get answered and more human corrections get logged.

This is where the architecture question compounds into a real competitive gap. If every customer's usage, every answered questionnaire, and every human correction feeds back into improving the underlying system, accuracy isn't a fixed number you buy once. It's a curve. Wolfia is built around that compounding effect: fact extraction plus a self-maintaining knowledge base that updates as source documents change, so answers don't quietly go stale between reviews, and each human correction further validates the library rather than getting lost after a single questionnaire cycle.

Apply this to Conveyor, to Wolfia, or to any tool in a bake-off. It's designed to be vendor-neutral, because the goal is an honest evaluation, not a predetermined conclusion.

1. Sourcing. Can the vendor show you, live, which specific document an answer traces back to?
2. Conflict handling. What happens when two source documents disagree? Does the tool flag it, or does it silently pick one?
3. Authority scoring. Does the system treat a signed contract and a stale wiki page as equally reliable, or does it weight sources?
4. Compounding. Does usage improve the system over time, or does accuracy stay flat without manual re-tagging?
5. Review step. Is there a human-in-the-loop approval gate before an answer ships back to a customer, or does the tool auto-send?

A vendor that answers all five with specifics, not marketing language, is one worth trusting with your questionnaire volume. A vendor that answers with a benchmark percentage and nothing else hasn't actually answered the question.

Conveyor's product is built around an AI layer over a customer's document library, with credit-based usage tiers gating volume. For GRC teams evaluating any AI questionnaire tool including Conveyor, the checklist above is the right lens: ask specifically how source attribution, conflict resolution, and improvement-over-time work in the product, rather than accepting an accuracy percentage at face value. Every vendor in this category, Wolfia included, should be willing to answer those questions concretely on your own documents in a live demo. For a deeper side-by-side on pricing structure and feature scope, see our Conveyor pricing and alternatives breakdown.

Wolfia is built for GRC and security teams who need to trust an automated answer without re-verifying it from scratch every time. The architecture maps directly to the checklist above:

• Fact extraction with source citations. Every answer traces to the approved source document it came from, visible to the reviewer, not buried in a black box.
• Conflict resolution by source authority. When two documents disagree, Wolfia surfaces the contradiction and scores it by source authority instead of guessing.
• A self-maintaining knowledge base. The answer library updates as source documents change, so answers don't drift out of date between refresh cycles.
• Human-in-the-loop review. A subject-matter expert approves each answer before it ships back to a customer, through the same Portal Agent workflow that auto-fills questionnaires across major vendor portals like OneTrust and ServiceNow, with the answer auto-routed to a reviewer before it leaves the building.
• Data network effects. Every customer, every answered questionnaire, and every human correction feeds back into the system, so accuracy compounds instead of staying flat.

This is the same distinction covered in more depth in our piece on verbatim-sourced versus agentic AI for compliance answers: grounded extraction with citations behaves differently under audit than free-form generation, even when both are labeled "AI-powered." If you've been burned by a tool that produced confident, wrong answers, our post on diagnosing inaccurate vendor security questionnaire answers walks through the failure patterns in more detail.

None of this is a claim that Conveyor or any other vendor lacks these mechanisms entirely. It's a claim that these five mechanisms are the actual determinants of accuracy, and any buyer evaluating tools in this category, whether that's Conveyor, Wolfia, or a third option, should ask to see them working on real documents before signing a contract. The NIST AI Risk Management Framework makes a version of the same point for AI systems generally: trustworthiness has to be evaluated at the level of the system's design and testing, not asserted from a vendor's marketing copy.

The instinct to compare AI questionnaire tools by asking "whose AI is better" leads to a dead end, because the model is rarely the differentiator anymore. The real differentiator is what happens before and after the model runs: whether the answer is grounded in an approved source, whether conflicting sources get resolved by authority instead of guesswork, whether a human reviews the answer before it ships, and whether the system gets better with every questionnaire it processes. Run the five-point checklist above against Conveyor, against Wolfia, and against anything else in the bake-off. The vendor willing to answer it concretely, on your own documents, is the one worth trusting with your next 200-question deadline.